Integrating WCET and timing analysis into embedded CI: lessons from RocqStat and VectorCAST

Hook: Stop timing regressions from derailing releases

Every release of an embedded system carries a hidden risk: a small code change that introduces a timing regression. For safety-critical devices — automotive ECUs, avionics, industrial controllers — an unnoticed worst-case execution time (WCET) increase can break real-time constraints, trigger costly rework, or invalidate certification artifacts. In 2026 those risks are amplified: systems are more software-defined, toolchains are consolidating (Vector's acquisition of RocqStat in January 2026 is a clear sign), and regulatory scrutiny is rising. If your CI pipeline can't automatically detect and document timing regressions, you're flying blind.

Executive summary: What this guide delivers

This article explains how to integrate WCET and timing analysis into embedded CI pipelines using modern tooling patterns — illustrated by the recent integration of RocqStat into VectorCAST. You'll get practical pipeline recipes, CI snippets, measurement best practices, and a repeatable approach to produce certification-ready artifacts automatically. Expect actionable advice you can apply to PR gating, nightly regression detection, and automated evidence collection for DO-178C and ISO 26262 workflows.

Why now? 2026 trends driving embedded timing automation

• Tool consolidation: Vector's January 2026 acquisition of StatInf's RocqStat and roadmap to integrate it into VectorCAST promises a unified verification and timing toolchain, reducing friction between unit testing and WCET analysis (Automotive World, Jan 16, 2026).
• Rising software complexity: Software-defined features and over-the-air updates increase change velocity — making continuous timing checks essential.
• Regulatory pressure: Certification bodies expect traceable evidence and reproducible timing analyses. Automating artifacts reduces manual certification cost and audit risk.
• CI maturity: Teams now expect CI to validate functional, safety, performance, and timing constraints — not just compile and unit tests. For cloud-based testbeds and low-latency simulation, consider architectures described in Edge Containers & Low‑Latency Architectures for Cloud Testbeds.

Core concepts before you automate

To integrate timing analysis into CI successfully, align your team on four core concepts.

1. Determinism vs. variability

Determinism is the foundation of meaningful WCET analysis. Static WCET tools (like RocqStat's static path analyses) and dynamic measurement differ: static tools reason about possible paths and hardware models to give safe upper bounds; measurements give empirical evidence but can miss rare worst-cases. A robust pipeline uses both: static estimates for safety margins and instrumented measurements to catch regressions and validate assumptions.

2. Execution environment matters

Results differ between QEMU, cycle-accurate simulators, and real hardware (HIL). Decide which environment is authoritative for gating. In many programs: PRs run fast static checks (emulator or model), nightly jobs run measured timing on representative hardware, and release candidates run exhaustive WCET analysis on qualified toolchains. If you’re running HIL farms or cloud testbeds, read about operational patterns for edge containers & low-latency architectures that help scale measured runs.

3. Traceability and provenance

Certification requires evidence: tool versions, compiler flags, map files, measurement logs, and test vectors. Your CI must record provenance automatically and store artifacts immutably. For organizing decision planes and audit trails, see the operational playbook for edge auditability & decision planes.

4. Threshold policy

Define how much deviation is acceptable. Typical policies: fail PR if WCET increases > 2% or > configured delta (ms), create warnings for marginal increases, and require explicit sign-off for larger regression. Automate these thresholds in CI checks. A practical tool sprawl audit helps prevent unchecked tool versions from invalidating thresholds.

Practical pipeline patterns

Below are pragmatic pipeline patterns you can adopt immediately. Each pattern maps to a stage in modern CI/CD.

Pattern A — Fast static WCET verification on pull requests

Goal: provide immediate feedback without long-running hardware tests.

1. Run a static WCET invocation (RocqStat CLI or equivalent) on the changed module(s).
2. Compare results against stored baseline WCET values for those functions.
3. Fail the PR on > threshold delta (e.g., 2%).

# example: minimal PR task (pseudocode)
wcet-cli analyze --project componentA --config wcet-config.json --out result.json
python tools/wcet_check.py --baseline artifacts/baseline/componentA.json --candidate result.json --threshold 0.02

Pattern B — Nightly measured timing on representative hardware

Goal: catch regressions caused by interaction effects, compiler changes, or hardware-dependent behavior.

• Run the full test harness on HIL (or JESD-compliant board farm) that captures hardware traces.
• Use instrumentation (trace, cycle counters) and the timing analysis tool to produce WCET estimates and measurement histograms.
• Record detailed logs, toolchain metadata, and the test vector set as artifacts.

Pattern C — Release candidate exhaustive analysis for certification

Goal: produce certification artifacts automatically.

1. Run full static WCET with annotation and micro-architectural models (cache, pipeline) using qualified toolchain.
2. Generate traceability matrix linking requirements & tests to timing results.
3. Package and sign artifacts for auditors.

CI implementation: example with GitHub Actions

Below is a pragmatic, simplified GitHub Actions workflow that runs a static WCET check on PRs and posts a pass/fail check. Replace wcet-cli with your vendor CLI (VectorCAST/RocqStat) and add authentication as needed. For improving the developer experience around these checks, review patterns in Edge‑First Developer Experience.

name: WCET PR Check

on:
  pull_request:

jobs:
  wcet-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup toolchain
        run: |
          sudo apt-get update && sudo apt-get install -y build-essential
          # install wcet-cli or VectorCAST runner
      - name: Build
        run: make all
      - name: Run static WCET
        run: |
          wcet-cli analyze --project src/component --config wcet.json --out wcet-result.json
      - name: Compare with baseline
        run: |
          python tools/wcet_check.py --baseline baselines/component.json --candidate wcet-result.json --threshold 0.02

Dealing with measurement noise: practical tips

Timing measurements are noisy. Here are proven techniques to reduce variability and get repeatable results.

• Control the platform: disable frequency scaling, turbo modes, and power-saving on measurement nodes. Lock CPU speeds and set interrupts to deterministic priority.
• Isolate tests: run timing tests on otherwise idle cores or use CPU shielding to prevent background tasks from perturbing measures.
• Use hardware timers and tracing: rely on on-chip cycle counters, instruction counters, or ETM traces rather than wall-clock timers.
• Warm-up runs: perform several warm-up iterations to get caches and predictors into representative states, then collect measured runs.
• Statistical reporting: store percentiles (median, 95th, 99.9th) and histograms rather than single numbers. Use p-values for regression detection where appropriate.

Linking timing results with regression tests and determinism checks

To be actionable, timing checks must be tied to tests. The most effective approach maps test cases to timing measurements:

1. Each unit/integration test provides an execution trace and measured WCET for the exercised path(s).
2. Store test-to-function mapping in an index so when a test fails a timing check, you can see affected requirements and code locations.
3. Automate alerts and create issues for PR authors with the exact failing test, measured value, baseline, and a small reproducer to speed triage.

Automating certification artifacts

Auditors ask for reproducibility and provenance. Integrate these elements into the CI so artifacts are produced automatically:

• Tool and environment snapshot: record tool versions (VectorCAST, RocqStat), OS images, compiler versions, and exact build commands.
• Measurement logs: raw traces, parsed WCET reports, and histogram CSVs.
• Traceability matrix: link requirements, tests, code, and timing results.
• Reproducer scripts: scripts that re-run the exact analysis from a known commit hash and configuration.
• Immutable storage: push artifacts to an artifact repository with retention policies and cryptographic signing where required — and remember that storage & jurisdiction policies (for instance, EU data residency rules) may affect where you keep signed artifacts (see data residency notes).

These artifacts form the backbone of certification packages for DO-178C, ISO 26262, and similar standards. Automating them reduces manual evidence collection and speeds audits. For operational decision planes and audit trails, review edge auditability & decision planes.

Example: wcet_check.py (regression gate script)

#!/usr/bin/env python3
import json,sys

baseline = json.load(open(sys.argv[1]))
candidate = json.load(open(sys.argv[2]))
threshold = float(sys.argv[3])

for fn,base_val in baseline.items():
    cand_val = candidate.get(fn, None)
    if cand_val is None:
        print(f"MISSING: {fn}")
        continue
    delta = (cand_val - base_val)/base_val
    if delta > threshold:
        print(f"REGRESSION: {fn} baseline={base_val:.6f} cand={cand_val:.6f} delta={delta:.3%}")
        sys.exit(2)
print("OK: no regressions")

Hypothetical case study: ECU braking control module

Scenario: an ECU braking control module has a historically measured WCET of 2.30 ms for the brake logic runnable. The team wants every PR to be blocked if WCET increases beyond 5% (2.415 ms).

1. Baseline: store 2.30 ms baseline in baselines/brake.json.
2. PR check: run static WCET analysis for modified files; if the candidate WCET > 2.415 ms, the PR fails with a diagnostic report including the path and contributing hot spots.
3. Nightly hardware tests: run the full integration test on HIL, collect histograms. If the 99.9th percentile exceeds threshold, open a high-priority ticket and attach full traces for developers.
4. Release: perform exhaustive static analysis with qualified toolchain and archive artifacts for auditors. Include tool qualification evidence, configuration files, and signed logs.

Toolchain qualification and trust

For certification, many programs must qualify tools or demonstrate they are used in a way that doesn't compromise evidence. When integrating commercial tools like VectorCAST and RocqStat into CI, ensure you:

• Record and lock tool versions in CI images.
• Capture deterministic inputs and seed values used by any probabilistic analyses.
• Maintain a qualified tool qualification kit if required by your standard.
• Document the role of the tool in the verification process and provide reproducible replays of analysis.

Common pitfalls and how to avoid them

• Pitfall: Relying only on measurements. Fix: combine static and dynamic methods.
• Pitfall: No artifact provenance. Fix: automatically attach toolchain metadata to every WCET result.
• Pitfall: Overzealous gating that blocks harmless updates. Fix: use staged policies: warnings for small deltas, failures for critical ones.
• Pitfall: Running on non-representative hardware. Fix: maintain a hardware profile matrix and tag authoritative platforms.

Future predictions (late 2025 – 2026 and beyond)

Based on the Vector–RocqStat consolidation and industry signals in late 2025 / early 2026, expect the following trends:

• Tighter integration of timing analysis and coverage testing inside unified toolchains (fewer handoffs between testing and timing teams).
• Tool-assisted audits: automated evidence generation and auditor-friendly export formats will reduce certification cycle time.
• ML-augmented timing analysis: data-driven models will help prioritize code paths that matter most for WCET regressions (not replace formal analysis but augment it).
• Edge telemetry: production telemetry will feed back into CI to calibrate realistic workload models for WCET analysis in later cycles.

"Timing safety is becoming a critical element of software verification" — automotive and avionics tool vendors are consolidating to deliver unified verification and timing workflows (Automotive World, Jan 16, 2026).

Actionable takeaways (quick checklist)

• Start small: add static WCET checks to PR pipelines for high-risk modules.
• Automate provenance: record tool versions, compilers, flags, and timestamps for every result.
• Use staged policies: warnings for small changes, failures for critical regression thresholds.
• Schedule nightly measured tests on representative hardware and store raw traces.
• Package reproducible artifacts for certification and automate their archival and signing.

Next steps: integrating RocqStat and VectorCAST in your pipeline

With Vector's acquisition of RocqStat and the roadmap to integrate it into VectorCAST, teams have a tangible path toward a unified environment that couples testing, coverage, and timing analysis. If you're evaluating how to adopt this model in 2026, consider a phased pilot: pick a critical module, integrate static checks in PRs, automate nightly measured runs, then expand to release candidate analysis with artifact generation for certification.

Call to action

If you need a practical, hands-on plan adapted to your toolchain and certification goals, our team at Florence.Cloud helps embedded teams integrate WCET and timing verification into CI pipelines, build reproducible certification artifacts, and design pragmatic gating policies. Contact us to run a proof-of-concept that uses your codebase, toolchain (VectorCAST / RocqStat or equivalent), and hardware to demonstrate automated timing regression detection within your CI workflow.

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